The deep learning concept has been around for decades, but it’s only more recently that it’s started to gain some serious traction.

Put simply, deep learning is all about data. And it’s a machine learning model inspired by the human brain. Sounds clever, right? It is. In fact, it’s a great place for service providers like you to start focusing on, if you’re not already.

The technique involves building a neural network and then training it on a dataset with a variety of recognition tasks. That could be anything from object identification in imagery, learning about intention from expressions to recognising trends in data.

The reason your clients need it? It saves huge amounts of time and the results are more accurate than ever – it’s essentially starting to outperform humans.

So, whether deep learning is still on your list of things to find out more about, or you want to discover why it might be of importance to your clients in the future, here are four articles we think you should read:

In this article, you’ll get definitions of both deep learning and reinforcement learning and gain an understanding of how they can be used. Both are machine learning functions that have the ability to enable computers to develop rules to solve problems, on their own.

With AI becoming increasingly commonplace, chances are you regularly encounter it in your day-to-day life, even if you don’t realise it – and that includes deep learning. This article discusses how sophisticated AI systems are in fact driven by this function and looks at what we might expect in the future.

As clever as AI is, it still needs a helping hand. If you take the concept of deep learning to mean analysing past data to predict and understand the future, then machine learning could risk returning results that are simply a product of its own environment. But that’s where supervised learning comes in.

Killer AI, robots making us obsolete – these are the kinds of stories floating around the world of buzz right now, but do these views really help us understand what we’re really dealing with? This article examines what deep learning can and cannot do, and why hype is a problem.